TII-SSRC-23 dataset- edited

ABSTRACT Intrusion Detection Systems (IDSs) are widely used to monitor and protect computer networks, but they often rely on very sensitive traffic data. Handling this kind of data may expose private information or even cause breaches if not treated carefully. To deal with this issue, this work introduces an adaptive hybrid obfuscation model designed to protect data privacy while keeping good detection accuracy for Machine Learning (ML)-based IDS. The model includes three different obfuscation levels\u2014mild, moderate, and strong\u2014each mixing hashing, noise, binning, and masking methods to change the data at different strengths. The researchers tested the framework using the TII-SSRC-23 dataset, after balancing it through under sampling and synthetic oversampling to make the classes balanced. The evaluation was done with TabNet as the main classifier and compared with other models like XGBoost, LightGBM, and k-Nearest Neighbors to verify robustness. Results showed that moderate obfuscation gives the best trade-off between privacy and performance, keeping accuracy around 85%, while strong obfuscation lowered accuracy to about 52%. Overall, the findings show that the proposed method can preserve data privacy without fully breaking model performance, giving a practical step toward safer and more privacy-aware IDS designs.